code: def testConstructsMinMaxVarsCorrectShape(self, layer_type, kwargs):
      quantizer = Default8BitConvWeightsQuantizer()
  
      model = keras.Sequential(
          [layer_type(input_shape=(5, 2, 3), **kwargs)]
      )
      layer = model.layers[0]
  
      min_max_vars = quantizer.build(
          layer.weights[0].shape, "kernel", layer
      )
      # TODO(pulkitb): Add value test to ensure per-axis quantization is
      # happening properly. Probably to quant_ops_test.py
      quantized_weight = quantizer(
          layer.weights[0],
          True,  # pylint: disable=unused-variable
          weights=min_max_vars,
      )
  
      min_var = min_max_vars["min_var"]
      max_var = min_max_vars["max_var"]
      self.assertEqual(5, min_var.shape)
      self.assertEqual(5, max_var.shape)
  
file_path: model-optimization/tensorflow_model_optimization/python/core/quantization/keras/default_8bit/default_8bit_quantizers_test.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  from absl.testing import parameterized
  import tensorflow
  from tensorflow_model_optimization.python.core.quantization.keras.default_8bit import default_8bit_quantizers
label_desc: Use image node_id 1 for calling the Default8BitConvWeightsQuantizerTest obj's underlying member method code with example usage: obj.testConstructsMinMaxVarsCorrectShape(layer_type, kwargs) without return types